More than half of customer service organizations are expected to double their technology spend by 2028, but here’s the part that should catch your attention: they’re not cutting headcount to pay for it. According to new research from Gartner, companies betting on AI to reduce labor costs are finding a very different reality; one where human talent becomes more important, not less. For operations focused on consumer engagement, including collections, this raises a critical question: are you investing in AI to replace people, or to make them more valuable?
Gartner’s findings highlight a clear disconnect between expectations and reality. While more than 50% of organizations are expected to double their technology spend by 2028, only 20% have actually reduced agent headcount due to AI. At the same time, nearly 80% of organizations plan to redeploy agents into new roles, and 84% expect to add new skills to frontline positions. The takeaway is straightforward: AI is not eliminating roles, it is reshaping them. As one analyst noted, talent needs are evolving, not disappearing.
There is growing pressure across industries to justify AI investments with immediate cost savings, but Gartner warns that moving too quickly can create real operational risk. Organizations that reduce headcount too aggressively may face service disruptions, lower customer satisfaction, increased complaints, and even compliance exposure. Some companies that have already pursued this path have been forced to reverse course, rehiring staff after automation initiatives failed to meet expectations. For collection operations, where conversations often require nuance, negotiation, and empathy, these risks are even more pronounced.
Another key insight is that AI is not simply a technology expense, it is an operational transformation. Beyond the cost of software, organizations must invest in integration, infrastructure, data quality, and knowledge management. They must also support ongoing training, monitoring, and governance, while introducing new roles such as analysts and AI oversight functions. In practice, AI does not reduce complexity, it redistributes it across the organization.
For collection operations, this research serves as a reality check. AI can absolutely improve efficiency and handle volume, but it is not a replacement for human expertise. The most effective strategies will involve using AI to manage routine interactions while shifting agents toward higher-value work such as complex negotiations, escalations, and exception handling. Investing in training will be just as important as investing in technology, and maintaining access to a human agent will remain critical, particularly in high-stress or sensitive consumer interactions.
AI is changing the economics of customer service, but it is not eliminating the need for people. Instead, it is raising expectations for how those people contribute. The organizations that succeed will not be the ones that cut the fastest, but the ones that adapt the smartest, aligning technology investments with a workforce that is equipped to deliver better outcomes.
